Low Latency Gaze Tracking via Latent Optical Sensing

arXiv:2605.17990 · cs.CV, cs.HC · Submitted 2026-05-18 · Read on arXiv

cs.CV, cs.HC

Submitted: 2026-05-18

Updated: 2026-09-27

Code: https://github.com/black-forest-labs/flux

Terminology

• Steering — 15× in this paper · explained in 2 episodes
• Angular Error — 12× in this paper · explained in 1 episode
• Accuracy — 10× in this paper · explained in 1 episode
• Latent Space — 10× in this paper · explained in 7 episodes
• Calibration — 9× in this paper · explained in 7 episodes
• Latent Representation — 7× in this paper · explained in 2 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Aperture — 5× in this paper · explained in 1 episode
• Distillation — 5× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Verification — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Energy Efficiency — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Personalization — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transactions on Pattern Analysis and Machine Intelligence — 1× in this paper
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes

Sources

• WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization
• JaneEye: A 12-nm 2K-FPS 18.9-$\mu$J/Frame Event-based Eye Tracking Accelerator
• A Differentiable Wave Optics Model for End-to-End Computational Imaging System Optimization
• Tolerance-Aware Deep Optics
• Investigating Bias and Fairness in Appearance-based Gaze Estimation
• One Eye is All You Need: Lightweight Ensembles for Gaze Estimation with Single Encoders
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models